• Characteristics of patients according to the mode of admission to regional stroke services

      Price, Christopher; Rae, V.; Duckett, Jay; Wood, R.; McMeekin, Peter; Gray, J.; Rodgers, Helen; Ford, Gary A. (2012-12)
    • Development and validation of a pragmatic prehospital tool to identify stroke mimic patients

      McClelland, Graham; Rodgers, Helen; Flynn, Darren; Price, Christopher (2018-04)
      Aim Stroke mimics (SM) are non-stroke conditions producing stroke-like symptoms. Prehospital stroke identification tools prioritise sensitivity over specificity.1 It is estimated that >25% of prehospital suspected stroke patients are SM.2 Failure to identify SM creates inefficient use of ambulances and specialist stroke services. We developed a pragmatic tool to identify SM amongst suspected prehospital stroke patients. Method The tool was developed using regression analysis of clinical variables documented in ambulance records of suspected stroke patients linked to primary hospital diagnoses (derivation dataset, n=1,650, 40% SM).3 It was refined using feedback from paramedics (n=3) and hospital clinicians (n=9), and analysis of an expanded prehospital derivation dataset (n=3,797, 41% SM (original 1650 patients included)). Results The STEAM tool combines six variables: 1 point for Systolic blood pressure <90 mmHg; 1 point for Temperature >38.5°C with Abstracts A2 BMJ Open 2018;8(Suppl 1):A1–A34 (NHS). Protected by copyright. on 14 August 2019 at Manchester University NHS Foundation Trust http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2018-EMS.6 on 16 April 2018. Downloaded from heart rate >90 bpm; 1 point for seizures or 2 points for seizures with known diagnosis of Epilepsy; 1 point for Age <40 years or 2 points for age <30 years; 1 point for headache with known diagnosis of Migraine; 1 point for FAST-ve. A score of 2 on STEAM predicted SM diagnosis in the derivation dataset with 5.5% sensitivity, 99.6% specificity and positive predictive value (PPV) of 91.4%. External validation (n=1,848, 33% SM) showed 5.5% sensitivity, 99.4% specificity and a PPV of 82.5%. Conclusion STEAM uses common clinical characteristics to identify SM patients with high certainty. The benefits of using STEAM to reduce SM admissions to stroke services need to be weighed up against delayed admissions for stroke patients wrongly identified as SM. https://bmjopen.bmj.com/content/8/Suppl_1/A2.3 This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ http://dx.doi.org/10.1136/bmjopen-2018-EMS.6
    • Development of a prehospital assessment to identify stroke mimic conditions

      McClelland, Graham; Rodgers, Helen; Flynn, Darren; Price, Christopher (2017-10)
      Background Despite routine use of pre-hospital identification instruments, approximately 30% of suspected stroke admissions are stroke mimics (SM). Early identification may allow “false positive” SM patients to be directed to appropriate care and improve healthcare resource utilisation. Methods A retrospective database of ambulance records containing a paramedic impression of stroke was linked to hospital specialist diagnosis data from 01/06/13 to 31/05/16. Logistic regression identified clinical features predictive of SM. An assessment score was constructed prioritising specificity over sensitivity. Results 1650 patients (mean age 75.3, 47% male, 40% SM) were included. 1520 (92%) were Face Arm Speech Test (FAST) positive. Table 1 describes the characteristics in the SM assessment. Each characteristic scores 1 point if present. Table 1 Stroke mimic characteristics 86% (66/77) of suspected stroke patients scoring 1 were SM. 100% (6/6) of patients scoring >1 characteristic were SM. A score ≥1 identified SM with 11% (95% CI, 8–13) sensitivity, 99% (95% CI, 98–99) specificity, positive predictive value of 87% (95% CI, 79–94), negative predictive value of 62% (95% CI, 60–64) and a diagnostic odds ratio of 11 (95% CI, 6–20, p<0.0001). Conclusions Amongst ambulance patients with suspected stroke, a small number of SM can be identified with a high degree of certainty. This simple tool needs further validation, prospective testing in the pre-hospital environment with characteristics systematically recorded and consideration of potential clinical impact. https://emj.bmj.com/content/34/10/e5.1 This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ http://dx.doi.org/10.1136/emermed-2017-207114.14
    • Development of a prehospital stroke mimic identification tool: a focus group study with healthcare professionals

      McClelland, Graham; Flynn, Darren; Rodgers, Helen; Price, Christopher (2017-11)
    • Effect of an Enhanced Paramedic Acute Stroke Treatment Assessment on Thrombolysis Delivery During Emergency Stroke Care: A Cluster Randomized Clinical Trial

      Price, Christopher; Shaw, Lisa; Islam, Saiful; Javanbakht, Mehdi; Watkins, Alan; McMeakin, Peter; Snooks, Helen; Flynn, Darren; Francis, Richard; Lakey, Rachel; et al. (2020-07)
    • Effect of an Enhanced Paramedic Acute Stroke Treatment Assessment on Thrombolysis Delivery During Emergency Stroke Care: A Cluster Randomized Clinical Trial

      Price, Christopher; Shaw, L.; Islam, Saiful; Javanbakht, Mehdi; Watkins, Alan; McKeekin, Peter; Snooks, Helen; Flynn, Darren; Francis, Richard; Lakey, Rachel; et al. (2020-04-13)
    • The frequency, characteristics and aetiology of stroke mimic presentations: a narrative review

      McClelland, Graham; Rodgers, Helen; Flynn, Darren; Price, Christopher (2019-02)
    • An observational study of patient characteristics associated with the mode of admission to acute stroke services in North East, England

      Price, Christopher; Rae, Victoria; Duckett, Jay; Wood, Ruth; Gray, Joanne; McMeekin, Peter; Rodgers, Helen; Portas, Karen; Ford, Gary A. (2013-10)
    • Paramedic acute stroke treatment assessment (PASTA): study protocol for a randomised controlled trial

      Price, Christopher; Shaw, Lisa; Dodd, Peter; Exley, Catherine; Flynn, Darren; Francis, Richard; Islam, Saiful; Javanbakht, Mehdi; Lakey, Rachel; Lally, Joanne; et al. (2019-02)
    • Positive predictive value of stroke identification by ambulance clinicians in North East England: a service evaluation

      McClelland, Graham; Flynn, Darren; Rodgers, Helen; Price, Christopher (2020-05-08)
      Accurate prehospital identification of patients who had an acute stroke enables rapid conveyance to specialist units for time-dependent treatments such as thrombolysis and thrombectomy. Misidentification leads to patients who had a ‘stroke mimic’ (SM) being inappropriately triaged to specialist units. We evaluated the positive predictive value (PPV) of prehospital stroke identification by ambulance clinicians in the North East of England. https://emj.bmj.com/content/37/8/474. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ https://https://emj.bmj.com/content/37/8/474
    • Positive Predictive Value of Stroke Identification by Ambulance Clinicians in North East England: A Service Evaluation

      McClelland, Graham; Flynn, Darren; Rodgers, Helen; Price, Christopher (2020-05-08)
      Introduction/background Accurate prehospital identification of patients who had an acute stroke enables rapid conveyance to specialist units for time-dependent treatments such as thrombolysis and thrombectomy. Misidentification leads to patients who had a ‘stroke mimic’ (SM) being inappropriately triaged to specialist units. We evaluated the positive predictive value (PPV) of prehospital stroke identification by ambulance clinicians in the North East of England. Methods This service evaluation linked routinely collected records from a UK regional ambulance service identifying adults with any clinical impression of suspected stroke to diagnostic data from four National Health Service hospital trusts between 1 June 2013 and 31 May 2016. The reference standard for a confirmed stroke diagnosis was inclusion in Sentinel Stroke National Audit Programme data or a hospital diagnosis of stroke or transient ischaemic attack in Hospital Episode Statistics. PPV was calculated as a measure of diagnostic accuracy. Results Ambulance clinicians in North East England identified 5645 patients who had a suspected stroke (mean age 73.2 years, 48% male). At least one Face Arm Speech Test (FAST) symptom was documented for 93% of patients who had a suspected stroke but a positive FAST was only documented for 51%. Stroke, or transient ischaemic attack, was the final diagnosis for 3483 (62%) patients. SM (false positives) accounted for 38% of suspected strokes identified by ambulance clinicians and included a wide range of non-stroke diagnoses including infections, seizures and migraine. Discussion In this large multisite data set, identification of patients who had a stroke by ambulance clinicians had a PPV rate of 62% (95% CI 61 to 63). Most patients who had a suspected stroke had at least one FAST symptom, but failure to document a complete test was common. Training for stroke identification and SM rates need to be considered when planning service provision and capacity. http://dx.doi.org/10.1136/emermed-2019-208902. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
    • A review of enhanced paramedic roles during and after hospital handover of stroke, myocardial infarction and trauma patients

      Flynn, Darren; Francis, Richard; Robalino, Shannon; Lally, Joanne; Snooks, Helen; Rodgers, Helen; McClelland, Graham; Ford, Gary A.; Price, Christopher (2017-02)
    • A survey of pre-hospital stroke pathways used by UK ambulance services

      McClelland, Graham; Rodgers, Helen; Price, Christopher (2018-12)
    • A survey of UK paramedics' views about their stroke training, current practice and the identification of stroke mimics

      McClelland, Graham; Flynn, Darren; Rodgers, Helen; Price, Christopher (2017-06)
      Abstract published with permission. Aims ‐ Paramedics play a crucial role in identifying patients with suspected stroke and transporting them to appropriate acute care. Between 25% and 50% of suspected stroke patients are later diagnosed with a condition other than stroke known as a ‘stroke mimic’. If stroke mimics could be identified in the pre-hospital setting, unnecessary admissions to stroke units could potentially be avoided. This survey describes UK paramedics’ stroke training and practice, their knowledge about stroke mimic conditions and their thoughts about pre-hospital identification of these patients. Methods ‐ An online survey invitation was circulated to members within the UK College of Paramedics and promoted through social media (8 September 2016 and 23 October 2016). Topics included: stroke training; assessment of patients with suspected stroke; local practice; and knowledge about and identification of stroke mimics. Results ‐ There were 271 responses. Blank responses (39) and non-paramedic (1) responses were removed, leaving 231 responses from paramedics which equates to 2% of College of Paramedics membership and 1% of Health and Care Professions Council registered paramedics. The majority of respondents (78%) thought that they would benefit from more training on pre-hospital stroke care. Narrative comments focused on a desire to improve the assessment of suspected stroke patients and increase respondents’ knowledge about atypical stroke presentations and current stroke research. The Face Arm Speech Test was used by 97% of respondents to assess suspected stroke patients, although other tools such as Recognition of Stroke in the Emergency Room (17%) and Miami Emergency Neurological Deficit (11%) were also used. According to those responding, 50% of stroke patients were taken to emergency departments, 35% went straight to a stroke ward and 8% were taken directly to CT scan. Most respondents (65%) were aware of the term ‘stroke mimic’. Two-thirds of respondents (65%) thought a tool that predicted the likelihood of a suspected stroke being a stroke mimic would be useful in pre-hospital care. Conclusion ‐ This study reports a survey of UK paramedics’ views about the stroke care they provide. Conclusions are limited by the low number of responses. Assessment of suspected stroke patients was recognised as an important skill by paramedics and an area where many would like further training. Respondents’ current practice varied in terms of the stroke assessment tools used and whether suspected stroke patients were taken to the emergency department or direct to a stroke ward. A stroke mimic identification tool would be useful if it allowed stroke mimic patients to be directed to appropriate care, but it would need to have a high level of specificity and not adversely impact on time to treatment for true stroke patients.
    • A systematic review of the clinical and demographic characteristics of adult patients with stroke mimics

      McClelland, Graham; Flynn, Darren; Rodgers, Helen; Price, Christopher (2015-09-01)